The fields of artificial intelligence, machine learning, and quantum computing are witnessing significant advancements, with a common theme of leveraging graph-based models and quantum machine learning to improve performance and efficiency. Graph-based models are being increasingly used to store and process complex relational data, with applications in computer vision, natural language processing, and emotion recognition. Notable papers include Views, which proposes a hardware-friendly graph database model, and GLaRE, which proposes a graph-based landmark region embedding network for emotion recognition. The incorporation of graph-based models into foundation models is leading to improved performance and interpretability in various tasks. In the field of quantum machine learning, researchers are exploring the integration of quantum computing with classical machine learning techniques to enhance performance and robustness. The use of quantum latent distributions in deep generative models has shown promise in improving generative performance. Hybrid quantum-classical architectures have been proposed for sequence-based tasks to improve quantum fidelity and classical similarity. The field of machine unlearning is also rapidly evolving, with a focus on developing efficient and effective methods for removing the influence of specific data from trained models. Noteworthy papers include Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks and Module-Aware Parameter-Efficient Machine Unlearning on Transformers. Additionally, the field of graph-based machine learning and retrieval-augmented generation is rapidly evolving, with a focus on improving security, privacy, and efficiency. Researchers are exploring new architectures and techniques to enhance the performance and robustness of graph-based models, while also addressing the challenges of protecting sensitive information and preventing data leakage. Overall, these emerging trends are expected to have a significant impact on the development of more advanced and efficient models for complex data analysis.